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Displaying posts with tag: distributed (reset)

MariaDB CONNECT Storage Engine and engine condition pushdown
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Engine condition pushdown is a MySQL internal mechanism that is intended to avoid to send non matching rows from the storage engine to the SQL layer. This is very important when the storage engine involves traffic over the network. This mechanism was initially created to optimize MySQL Cluster (NDB) behavior. For the NDB storage engine [...]

Consistency across a WAN
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There are three solutions for providing consistency in a data service that operates across a wide area network (WAN). None of them are free. What are you willing to pay and where are you willing to add complexity? Depending on what you choose your system can be more complex for external users, internal application developers or operations. The choices are multi-master with conflict resolution (eventual consistency), multi-master with conflict prevention (strong consistency) and single master with downtime on failover.

If you choose eventual consistency (EC) then internal application developers must write logic to resolve conflicts and external users will occasionally encounter inconsistent data. This might be a small price to pay for a system that provides higher availability and transparent

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CAP for MySQL
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Stonebraker wrote a post on CAP. It is interesting because he highlights all of the causes for failures in a database service and then provides his estimates on the frequency of those causes. Many posts on CAP ignore failures caused by how the service is used and operated.
  • Choose CA - You can get CA (consistency, availability) for a database service running within a datacenter. Use DRBD with MySQL. Then use asynchronous replication to a remote datacenter to survive the loss of a datacenter. You don't get CA between datacenters in this setup. Others have written about the ability to get CA

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CAP theorem
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Do you want to reference the CAP theorem at your next tech conference? I certainly do. Consistency, availability and partition tolerance are wonderful. You only get two and sometimes you only get one. But what are they? In the CAP context availability and partition tolerance are labels for behavior defined by the CAP presentation and paper and this is the source of much confusion. Start with the original work before reading summaries by others.

Eric Brewer presented CAP in a keynote at the PODC. The best slides are on page 4. With XA you forfeit partitions and get CA (consistency + availability). With majority protocols you forfeit availability and get CP (consistency + partition tolerance). XA == CA is easy for me to remember and then I know that majority protocols provide CP.

CAP was proved



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Observations on key-value databases
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Key-value databases are catching fire these days. Memcached, Redis, Cassandra, Keyspace, Tokyo Tyrant, and a handful of others are surging in popularity, judging by the contents of my feed reader.

I find a number of things interesting about these tools.

  • There are many more of them than open-source traditional relational databases. (edit: I mean that there are many options that all seem similar to each other, instead of 3 or 4 standing out as the giants.)
  • It seems that a lot of people are simultaneously inventing solutions to their problems in private without being aware of each other, then open-sourcing the results. That points to a sudden sea change in architectures. Tipping points tend to be abrupt, which would explain isolated redundant development.
  • Many of the products are feature-rich with things programmers need: diverse
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Distributing my card...
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As I distribute my card, the most common question I run into is "what does MySQL AB do?"

My answer is usually something like "we allow you to store a lot of information and process it efficiently."

But I've been thinking a bit, and perhaps a better quip might be "you know what the Central Intelligence Agency is, right? Well, we are the most popular database system used by the Decentralized Intelligence Agency, as it were."

:)
Showing entries 1 to 6

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